Method for data processing and display for detecting and/or predicting possible trends

ABSTRACT

A systematic method for detecting trends based on outcomes generated by a first process, comprising: (a) determining all possible outcomes associated with the first process, wherein the first process is associated with a range of possible outcomes; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) allocating each identifier generated in step (d) to said matrix in accordance with the exeleon allocation procedure; and (g) repeating step (f) until a trend of duplicating identifiers becomes self evident.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority from U.S. Provisional Patent Application Ser. No. 60/675,097, filed Apr. 27, 2005, the entire contents of which are incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

FIELD OF THE INVENTION

This invention relates to data processing and display for detecting and/or predicting possible trends.

BACKGROUND OF THE INVENTION

Human minds typically have difficulty in quickly processing and making sense of large quantities of numeric and nonnumeric data, particularly in real time. The task of detecting trends in real time to enable rapid rational decisions is often very difficult.

While there are numerous prior art software techniques for handling large volumes of data, such techniques often do not prove useful or meaningful in displaying information in an easy to understand manner to help discern trends to provide a basis for making rational decision to predict likely future outcomes.

Thus, a methodology solving the aforementioned problems is desired.

SUMMARY

The present invention, and the exeleon algorithm in particular can be used as a systematic method for detecting trends based on outcomes generated by a first process, comprising: (a) determining all possible outcomes associated with the first process, wherein the first process is associated with a range of possible outcomes; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) allocating each identifier generated in step (d) to said matrix in accordance with the exeleon allocation procedure; and (g) repeating step (f) until a trend of duplicating identifiers becomes self evident. In addition, one or more future outcomes are predicted based on one or more duplicated identifiers appearing in at least two rows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 to 6 show various aspects of the application of the exeleon algorithm, according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The invention is directed to a method for detecting and/or predicting possible trends in numeric or non-numeric data in real time or based on historic data.

Attached pages labeled P1 through P15 constitute part of the detailed description of the invention.

WORKING EXAMPLE#1

Attached page labeled “Page A” shows an exemplary non-limiting example of how the invention works (referred to as the Exeleon Algorithm) as applied to numeric output from a European roulette wheel. The European roulette consists of 37 identical slots, individually numbered from 0, 1 through 36. In European roulette, zero (i.e., 0) is regarded as a number of no real consequence. Ignoring 0, only 36 outcomes are possible: 1-36.

The set of possible outcomes, (i.e., 1 through to 36) are coded in any suitable way. For example, the 36 possible outcomes could be grouped in nine (9) groups as shown at 100 a (and FIG. 1). Specifically, the 36 possible outcomes are displayed into three vertical columns further differentiated into 9 rows to provide 9 Groups, thus covering all 36 possible outcomes at 100 a. Each group is given a letter identifier at 100 b. For example, numbers 1, 4, 7, and 10 are grouped in Group A. The Groups thus range from A through I.

A data set of 24 consecutive numbers produced by spinning the European roulette wheel is represented by the alphanumeric label 200 a (and FIG. 2). The 24 consecutive numbers 200 a are coded at 200 b and are inserted sequentially into a novel 2D matrix at 200 c in accordance with the exeleon algorithm (i.e., exeleon procedure; TABLE 1 (FIG. 3) shows an exemplary example of the exeleon algorithm, which here is used to process the output data shown, for example, in FIG. 2 and at 200 b). The novel matrix 200 c consists of 22 blocks or fields: a first row or level (labeled L1) of nine numeric fields, a second row (labeled L2) of 6 numeric fields, a third row (labeled L3) of 4 fields, a fourth row (labeled L4) of two fields, and a fifth row (labeled L5) consisting of just one field (B1,L5 or L5,B1). However, the data set 100 a has 24 consecutive numbers and matrix 200 c has 22 blocks or fields, the overflow is accommodated at the end of L3 as shown (see matrix 200 c on attached page A).

Thus, the first nine numbers in the number series 200 a are inserted horizontally into L1; for example, the first number in the data set 200 a is “11” and this is coded as B and inserted into field B1,L1, or L1,B1; the second number in the data set 200 a is “30” and this is coded as I into field B2,L1 or L1,B2; and so on until the matrix 200 c is filled up (see Table 1 which shows an exemplary example of the exeleon algorithm of the invention which is used to generate matrix 200 c).

As the European roulette wheel is spun, the table 200 c fills up level by level. By level 4 (L4), a person might decide to bet on slots associated with letter codes F and E, i.e., slots 15, 18, 21, and 24; and 14, 17, 20, and 23, respectively. Thus, the output matrix 200 c at L4 can be used to unemotionally place bets on numbers of groups F and E.

The inventor has made the very unexpected discovery that the exeleon algorithm generates a matrix in the form of an exeleon configuration of 5,4,3,3,2,2,2,1,1 with respect to the number of cells in each column that contain about 90% of the expected output as generated by the exeleon algorithm. An example of exeleon configuration is shown in FIG. 4, wherein column B1 has five cells, B2 has 4 cells, B3 has 3 cells, B4 has 3 cells, B5 has 3 (instead of the expected 2) and likewise for B6, and B7 has but one of the expected two cells filled, and B8 and B9 are filled in accordance with the exeleon configuration of 5,4,3,3,2,2,2,1,1.

It should be understood that the roulette wheel examples as used herein, are non-limiting exemplary examples that show how the invention can be used to process data and suggest in real time how a person using the invention might make rational and/or unemotional decisions on selecting likely future outcomes.

For example, the present invention can be applied to detecting trends in shoppers' buying habits. Products on sale in supermarket aisle can be vertically coded in the manner shown at 100 a on attached page labeled A. Shoppers buying habits for the products so grouped can be monitored electronically at the store cash registers and predictions made as to which items are likely to require restocking first, second, third, etc.

The present invention, and the exeleon algorithm in particular can be used as a systematic method for detecting trends based on outcomes generated by a first process, comprising: (a) determining all possible outcomes associated with the first process, wherein the first process is associated with a range of possible outcomes; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) allocating each identifier generated in step (d) to said matrix in accordance with the exeleon allocation procedure; and (g) repeating step (f) until a trend of duplicating identifiers becomes self evident. In addition, one or more future outcomes are predicted based on one or more duplicated identifiers appearing in at least two rows.

In another non-limiting embodiment of the present invention, a systematic method is provided for detecting trends based on outcomes generated by a first process. The method includes the steps of: (a) determining all possible outcomes associated with the first process, wherein the first process is associated with a range of possible outcomes; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) allocating each identifier generated in step (d) to a row in said matrix and to a subsequent row in said matrix upon each occurrence of a duplicated identifier; and (g) repeating step (f) until a trend of duplicating identifiers becomes self evident. It should be noted some steps could be done in any order. For example, step (e) can be done as a first step.

In another example of the present invention, a systematic method is provided for predicting outcomes generated by a first process. This method includes the steps of: (a) determining all possible outcomes associated with the first process, wherein the first process is associated with a range of possible outcomes; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) allocating each identifier generated in step (d) to a row in said matrix and to a subsequent row in said matrix upon each occurrence of a duplicated identifier; and (g) predicting a future outcome based on one or more duplicated identifiers appearing in at least two preceding rows. It should be noted some steps could be done in any order. For example, step (e) can be done as a first step.

The present invention can be applied to a plethora of applications. For example, the exeleon algorithm, according to the present invention, can be applied to detect trends in failures in complex mechanical and/or electrical systems such as F18A Hornet aircraft. If there are hidden trends, the exeleon procedure can be applied to help detect failure trends and thereby predict likely future failures in specific components of the aircraft. Likewise, the present invention (and hence the exeleon algorithm) can be applied to detect trends and likely failures in any kind of complex aircraft such as the F15 Eagle, the F16 Falcon, and the Harrier jump jet, F14 Tomcat. Similarly, the present invention can be applied to detect failure trends in civilian aircraft such as Airbus (e.g., Airbus A320 family) and Boeing airliners such as the 747, 767, 777, and the upcoming Dreamliner Boeing, yet to go into production.

Exeleon Mathematical Algorithm

I want to patent a mathematical model or formula, to be called the exeleon algorithm, which is able to distinguish and characterise a certain inbred phenomena in probability events.

This exeleon mathematical formula or algorithm, will allow certain accurate predictions in probability events, randomness, chaos and complexity.

This mathematical formula differentiates numbers or events, or clusters of numbers and events in such a way, that a prediction of the outcome of random events can be made, with this formula, with a higher degree of accuracy than normal.

This mathematical formula, the exeleon model, monitors appearance and non appearance of event cycles in such a way that numbers and or events, or groups, or clusters of numbers and events can be labelled in a way, that differentiates one form another.

These labelled numbers or events or clusters of both can then be filtered through this mathematical sieve, to be called the exeleon sieve, which allows the different labelled events to appear in such a way that predictions of the outcome can be done with a high degree of accuracy.

This outcome can be plotted in a two dimensional matrix, which will visualize the filtering process of the exeleon sieve in such a way that certain predictions of forthcoming events can be made with a high percentage of accuracy.

This exeleon model operates with pseudo and true random events. This exeleon model can operate horizontally and vertically simultaneously and separately and will also operate in higher dimensional (3 and above ) mathematical probability models. This exeleon model can be used manually or be written as an algorithm formula in most computer software programmable languages.

This exeleon model will operate in all situations of probability, whether it involves predicting the outcome of an event of events, which can and cannot be done with standard mathematical formulas and or algorithms in all forms of science, gaming, gambling, weather predictions, life expectancies, business cycles, etc. All events where probability plays a role can be analysed with the exeleon algorithm, which will enhance the accuracy op predicting the appearance of forthcoming event or events.

The exeleon algorithm operates equally well with historical data, present and future data and also analyses all three simultaneously as the present becomes past, the future becomes present and a new future appears.

The two dimensional matrix mathematical sieve, the Exeleon sieve, has a basic format as in the drawing below, but can vary horizontally and vertically, in length and format depending on the amount of events to be analysed and or the the probability cycle of the events to be studied.

This two dimensional matrix can be extrapolated in higher dimensional models.

EXAMPLE OF THE EXELEON MATHEMATICAL SIEVE

EXAMPLE HOW THE EXELEON ALGORITHM WORKS WITH A 9 BLOCK PROBABILITY DISTRIBUTION

Probability numbers derived from a casino roulette wheel—Sequence 1

11,30,15,8,7,18,28,4,12,27,15,17,34,33,23,7,28,13,32,18,14,8,17,15 therefore 24 consecutive numbers

24 consecutive numbers needed to fill the Exeleon Algorithm for a 9 block format. 22/24*100=91,61% accurate with horizontal coding.

This is an example of horizontal coding—Sequence 1

Probability number derived from a casino roulette wheel-Sequence 1

11,30,15,8,7,18,28,4,12,27,15,17,34,33,23,7,28,13,32,18,14,8,17,15 Probability numbers of Sequence 1 coded to operate with a Exeleon Algorithm Quant Num V H 1 11 B G 2 30 I F 3 15 F B 4 8 B D 5 7 A D 6 18 F E 7 28 G C 8 4 A A 9 12 C G 10 27 I C 11 15 F B 12 17 E F 13 34 G I 14 33 I I 15 23 E H 16 7 A D 17 28 G C 18 13 D B 19 32 H F 20 18 F E 21 14 E B 22 8 B D 23 17 E E 24 15 F B

EXAMPLE HOW THE EXELEON ALGORITHM WORKS WITH A 9 BLOCK PROBABILITY DISTRIBUTION

Probability numbers derived from a casino roulette wheel—Sequence 2

2,9,3,36,5,7,8,26,19,1,32,36,21,9,12,23,25,6,21,5,25,14,20,9,2,17,7, 23 consecutive numbers

23 consecutive numbers needed to fill the Exeleon Algorithm in a 9 block format. 22/23*100=95,83% accurate with horizontal coding.

This is an example of horizontal coding for Sequence 2

The same 23 consecutive Probability numbers with vertical coding

2,9,3,36,5,7,8,26,19,1,32,36,21,9,12,23,25,6,21,5,25,14,20,9,2,17,7,

22/23*100=95,65% accurate with Vertical Coding

Probability Numbers Derived from a Casino Roulette Wheel—Sequence 2

2,9,3,36,5,7,8,26,19,1,32,36,21,9,12,23,25,6,21,5,25,14,20,9,2,17,7, Probability numbers of Sequence 2 coded to operate with a Exeleon Algorithm quant rob nur code h code v 1 2 B A 2 9 C G 3 3 C A 4 36 I I 5 5 B D 6 7 A D 7 8 B D 8 26 H C 9 19 D E 10 3 A A 11 32 H H 12 36 I I 13 21 F H 14 9 C G 15 12 C G 16 23 E H 17 25 G C 18 6 C D 19 21 F H 20 5 B D 21 25 G C 22 14 E B 23 20 E E

RESULTS OF SEQUENCE 1 & 2 PROBABILITY DISTRIBUTIONS WITH EXELEON ALGORITHM SEQ CODING B1 B2 B3 B4 B5 B6 B7 B8 B9 Series 1 1 H 5 4 3 3 3 3 1 1 1 Series 2 1 V 5 4 3 3 3 2 2 1 1 Series 3 2 H 5 4 4 3 2 2 2 1 1 Series 4 2 V 5 4 3 3 3 2 2 1 0 Series 5 Average H/V 5 4 3 3 3 2 2 1 1

Results of Sequence 1 & 2 Probability Distributions with Exeleon Algorithm.

Results of Sequence 1 & 2 Probability Distribution with Exeleon Algorithm. Cells B1 B2 B3 B4 B5 B6 B7 B8 B9 Correct result % 100 100 75 100 75 75 75 100 75

Extract of Simulation of Descrete Probabilities TABLE 1.1 Sample output of the program Random Numbers. n random real numbers were generated in the interval (0, 1) where n was chosen by the user. n = 20 Sample output as is published 203309 762057 151121 623868 932052 415178 716719 967412 O69664 670982 352320 O49723 750216 784810 O89734 966730 946708 380365 O27381 900794

This sample output was used as is and with another 2 more configurations to increase the probability 203309 762057 151121 623868 203309 932052 932052 415178 716719 967412 762057 415178 O69664 670982 352320 O49723 151121 716719 750216 784810 O89734 966730 623868 967412 946708 380365 O27381 900794 967412 O49723

These random sample lists were then analized with the Exeleon Algorithm and filled up the Exeleon matrix as follows: B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 203309 L1 2 0 3 9 5 6 4 7 1 8 932052 L2 3 0 9 2 6 5 4 7 O69664 L3 3 0 2 9 6 750216 L4 0 2 6 9 946708 L5 0 6 L6 0 RESULT S1 6 5 4 4 3 2 2 2 1 1 762057 L1 7 6 2 0 5 4 1 8 9 3 415178 L2 7 5 1 6 0 8 2 4 3 670982 L3 7 8 1 0 6 5 784810 L4 7 8 0 380365 L5 7 8 RESULT S2 5 5 4 3 3 3 2 2 2 1 151121 L1 1 5 2 7 6 9 3 0 8 4 716719 L2 1 7 5 2 3 0 9 8 352320 L3 1 2 7 3 0 O89734 L4 1 2 7 3 O27381 L5 1 L6 1 L7 1 RESULT S3 7 4 4 4 3 2 2 2 1 1 623868 L1 6 2 3 8 9 7 4 1 0 967412 L2 6 8 2 4 9 7 3 4 O49723 L3 6 2 9 7 3 0 966730 L4 6 9 0 7 900794 L5 6 0 9 RESULT S4 5 5 4 3 3 3 2 2 1 0 203309 L1 2 0 3 9 7 6 5 1 8 4 762057 L2 3 0 2 7 5 1 6 8 9 151121 L3 0 1 2 3 6 623868 L4 1 2 6 967412 L5 1 2 RESULT S5 5 5 4 3 3 2 2 2 2 1 932052 L1 9 3 2 0 5 4 1 7 8 6 415178 L2 2 5 1 7 9 6 4 0 4 3 716719 L3 1 7 9 2 967412 L4 1 7 9 2 O49723 L5 1 7 RESULT S6 5 5 4 4 2 2 2 2 2 2

The same basic pattern was acheived with using single numbers from S1 to S6 with using the Exeleon Algorithm, to differentiate between the different appearance ratio's RESULT S1 6 5 4 4 3 2 2 2 1 1 RESULT S2 5 5 4 3 3 3 2 2 2 1 RESULT S3 7 4 4 4 3 2 2 2 1 1 RESULT S4 5 5 4 3 3 3 2 2 1 0 RESULT S5 5 5 4 3 3 2 2 2 2 1 RESULT S6 5 5 4 4 2 2 2 2 2 2

Comparison Between a 10 Block and a 9 Block Probability Pattern Configuration as is Filled up with the Exeleon Matrix Algorithm.

Results of Sequence 1 & 2 Roulette Probability Distributions with Exeleon Algorithm SEQ CODING B1 B2 B3 B4 B5 B6 B7 B8 B9 1 H 5 4 3 3 3 3 1 1 1 1 V 5 4 3 3 3 2 2 1 1 2 H 5 4 4 3 2 2 2 1 1 2 V 5 4 3 3 3 2 2 1 0 

1. A systematic method for detecting trends based on outcomes generated by a first process, comprising: (a) determining all possible outcomes associated with the first process, wherein the first process is associated with a range of possible outcomes; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) allocating each identifier generated in step (d) to said matrix in accordance with the exeleon algorithm; and (g) repeating step (f) until a trend of duplicating identifiers becomes self-evident.
 2. The method according to claim 1, wherein one or more future outcomes are predicted based on one or more duplicated identifiers appearing in at least two rows.
 3. A systematic method for detecting trends based on outcomes generated by a first process, comprising: (a) determining all possible outcomes associated with the first process, wherein the first process is associated with a range of possible outcomes; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) allocating each identifier generated in step (d) to a row in said matrix and to a subsequent row in said matrix upon each occurrence of a duplicated identifier; and (g) repeating step (f) until a trend of duplicating identifiers becomes self-evident.
 4. A systematic method for predicting outcomes generated by a first process, comprising: (a) determining all possible outcomes associated with the first process, wherein the first process is associated with a range of possible outcomes; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) allocating each identifier generated in step (d) to a row in said matrix and to a subsequent row in said matrix upon each occurrence of a duplicated identifier; and (g) predicting a future outcome based on one or more duplicated identifiers appearing in at least two rows. 